code: def plots_binary(target, predicted_labels, predicted_probas):
      figures = []
      try:
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_confusion_matrix(
              target, predicted_labels, normalize=False, ax=ax1
          )
          figures += [
              {
                  "title": "Confusion Matrix",
                  "fname": "confusion_matrix.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_confusion_matrix(
              target, predicted_labels, normalize=True, ax=ax1
          )
          figures += [
              {
                  "title": "Normalized Confusion Matrix",
                  "fname": "confusion_matrix_normalized.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_roc(target, predicted_probas, ax=ax1)
          figures += [
              {
                  "title": "ROC Curve",
                  "fname": "roc_curve.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_ks_statistic(
              target, predicted_probas, ax=ax1
          )
          figures += [
              {
                  "title": "Kolmogorov-Smirnov Statistic",
                  "fname": "ks_statistic.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_precision_recall(
              target, predicted_probas, ax=ax1
          )
          figures += [
              {
                  "title": "Precision-Recall Curve",
                  "fname": "precision_recall_curve.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          # transform target if needed to be {0, 1}
          target_uniq_values = np.unique(target)
          target_transformed = target.values.ravel()
          if not (0 in target_uniq_values and 1 in target_uniq_values):
              mapping = {
                  target_uniq_values[0]: 0,
                  target_uniq_values[1]: 1,
              }
              target_transformed = target.map(mapping)
          # create a plot
          _ = skplt.metrics.plot_calibration_curve(
              target_transformed,
              [predicted_probas],
              ["Classifier"],
              ax=ax1,
          )
          figures += [
              {
                  "title": "Calibration Curve",
                  "fname": "calibration_curve_curve.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_cumulative_gain(
              target, predicted_probas, ax=ax1
          )
          figures += [
              {
                  "title": "Cumulative Gains Curve",
                  "fname": "cumulative_gains_curve.png",
                  "figure": fig,
              }
          ]
          #
          fig = plt.figure(figsize=(10, 7))
          ax1 = fig.add_subplot(1, 1, 1)
          _ = skplt.metrics.plot_lift_curve(
              target, predicted_probas, ax=ax1
          )
          figures += [
              {
                  "title": "Lift Curve",
                  "fname": "lift_curve.png",
                  "figure": fig,
              }
          ]
  
      except Exception as e:
          print(str(e))
  
      return figures
  
file_path: mljar-supervised/supervised/utils/additional_plots.py
imports: import os
  import numpy
  import pandas
  import scikitplot
  from matplotlib import pyplot
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
label_desc: Use image node_id 1 for calling the AdditionalPlots obj's underlying member method code with example usage: obj.plots_binary(target, predicted_labels, predicted_probas) and returns: figures